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What can go wrong in a
distributed system
experience from the field
(the Hazelcast experience)
@AndriiRodionov
About me
- Software engineer at Hazelcast
- Data Engineering Certification program UCU
- National Technical University of Ukraine
- JUG UA Leader
- JavaDay Lviv and Devoxx Ukraine program
committee member
Transform Combine Stream ML Inference
Real-time Stream
Processing Engine
Durable
Persistence
In-memory
Fast Data
Clients
& APIs
Go JDBC
Java C#/.Net C++ JS Python
SQL
What is the Hazelcast platform?
Distributed
Map
Distributed
Lock,
AtomicRef
Real-time stream processing + In-memory data platform
Let me show what we can do!
Transform Combine Stream ML Inference
Hazelcast Stream
Processing Engine
Topic
Hazelcast Platform
Fast Data Store
Feature Store
(Feast)
Data pipeline
In Hazelcast we have our own
● network primitives
● serialization and schema catalog
● RPC sync/async model
● concurrency primitives
● distributed data structures
● best effort replication mechanism (AP data structures)
● Raft-based consensus protocol (CP data structures)
● real-time stream processing
Distributed systems postulates
● Communication speed is not instantaneous and is limited
by the speed of light
● Time is relative - there is no global time and two equal
clocks
● There is no way to distinguish network failure or remote
node failure (FLP theorem)
Distributed system pain points
● Replication
● Network
● Retries
● Leader based (Raft) algorithm
Replication
Replication
https://github.com/hazelcast/hazelcast/issues/18675
Why?
● Job state (snapshot) replication is based on IMap (distributed Hazelcast map)
● IMap is AP (best effort consistent) data structure
● It means it can only provide eventual consistency guaranties
Hz 1
Hz 2
Hz 3
Client 1
Client 2
write
read
write
ACK
write
write
ACK
with Err
read
backup for Hz 1
Boom!
What can we do?
Build your solution on top of reliable primitives
● Majority write + majority read
○ better, but …
○ there is still place for inconsistency
○ MongoDB and Cassandra have an additional linearizable mode
● Linearizability
○ one of consensus protocols should be used - Raft/ZAB/Paxos
Performance
Predictability
https://jepsen.io/consistency
Network
Hazelcast network
Outbound queue Inbound queue
Packets
Operations
Data
Operation + Data
https://twitter.com/mathiasverraes/status/632260618599403520
Network
Reordering
Reordering
https://github.com/hazelcast/hazelcast/issues/22713
JoinRequest
Hz 1 Hz 2
Hz 3
JoinRequest
{IP, UID}
Hz 1
Hz 2
Hz 3
Stale JoinRequest + reordering
restarted
x.x.1
x.x.2
x.x.3
JoinRequest
{x.x.3, UID}
JoinRequest
{x.x.3, newUID}
Boom!
What can we do?
Detect and ignore outdated messages
● use the Timestamp
● use logical clock
○ add a new version field to JoinRequest messages with some comparable
number that can help to detect outdated messages
● hold a list of all previously disjoined members
Retries
- Log pollution
- Disk out of space due to log pollution
- Retries amplification
- system going out of control due to cascading
uncontrolled and unstoppable retirees
- Out of memory
“Retries are similar to a powerful medicine - useful
in the right dose, but can cause significant damage
when used too much”
Amazon
Retries amplification
https://blog.pragmaticengineer.com/resiliency-in-distributed-systems/
OOM
https://github.com/hazelcast/hazelcast/issues/22135
Hazelcast network
Outbound queue Inbound queue
Packet
Packet
Packet
Packet Packet
Packet
Packet
Packet
Packet
Packet
Packet
Packet
Packet
Packet
Packet
What can we do?
Use defensive mechanisms
● Reduce message/packet size
● Limit retries
● Limit buffers/queues and apply drop policy for old/new messages
● Introduce flow-control or backpassure behaviour
Takeaways
● Based you solution on a reliable components (understand what reliability
means in your particular case)
● Think about messages order and delivery guaranties
● Never use unlimit timeouts, queues, buffers, messages size
● Limit retries - consider retries like a drugs
● Take into account network latency and different timeouts
● Test your system introducing a network latency, pausing/unpausing VMs
● Clients is also a part of your system
Stress test with latency
Takeaways
Read books
Questions?
@AndriiRodionov

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